Viral events monitoring

Some situations are described in public long before anything about them is measured. A fire, a closed road, a shortage — the first version arrives as a handful of posts and a paragraph of news, and what matters at that moment is not the detail but the direction: is this spreading, and from how many independent places?

Viral events monitoring watches the sources where things surface early and treats that spread as a signal worth tracking. Reports of the same happening are collected into one event, the event is scored, and it is carried into the platform as something with a place, a timeline and a level of confidence.

• Watched sources — news feeds and public posts, ingested continuously.
• Collected reports — many mentions of one happening become one event.
• Score — how fast and how widely the story is moving.
• Corroboration — how many independent sources are saying it.
• Timeline — updates appended as the story develops.

Articles and posts are ingested and enriched as they arrive: the text is read, the entities inside it are extracted, and a sentiment is attached to its framing. That work is what allows the platform to recognise that two differently worded reports concern the same thing, rather than storing them as two unrelated records.

Collecting reports is what turns a stream into a queue. A list of monitored headlines with no grouping is a search result; one event with a dozen mentions attached, each with its source and its timestamp, is something an operator can decide about.

• Ingestion — news and social sources polled on their own schedules.
• Enrichment — entities extracted and sentiment attached per item.
• Grouping — related mentions collected under one event record.
• Provenance — every mention keeps its source and its time.

Two things do most of the work. The first is how fast the story is moving: a burst of mentions inside an hour is a different situation from the same total counted over a week. The second is corroboration — a claim carried by several independent sources is treated differently from a claim carried by one.

Neither is a verdict, and the platform does not present it as one. A score moves an event up or down the queue; it does not decide whether the event is true, and it does not close the question of what should be done about it.

Volume is not truth and speed is not severity — the score tells you where to look, not what to conclude.

Detected events accumulate, so the console around them is built for several at once rather than one at a time: filter by status, search the set, sort by score or time, and select several events to act on together.

Each event also keeps its own timeline of updates, so the story can be read as it developed rather than in the state it happened to be in when someone last looked.

• Filter — by status, source and severity.
• Search — over titles, mentions and extracted entities.
• Sort — by score, recency or volume.
• Multi-select — several events handled in one action.
• Updates — the timeline grows with the story.

A viral event is not a separate kind of information; it is an early version of the same event the rest of the platform holds later. That is why detected events enter the shared queue, why their mentions are resolved into entities in the graph, and why the places they name appear on the same map as everything else.

The practical effect is continuity. A story watched on Tuesday morning is the same record that carries the measurements added on Thursday, rather than two systems each holding half of the picture.

• Event queue — detected events join the platform's tracked work.
• Graph — extracted entities link the story to real places and organisations.
• Map — a detected event can be read beside the layers that explain it.
• Reports — activity over a period is summarised in the situation report.

What spreads is not the same as what matters, and the platform is explicit about the difference: a story can be everywhere and still be wrong, and a quiet problem can be the one that deserves the morning. The queue orders attention; it does not supply judgement.

Sources are also uneven. Some publish quickly and revise later, some publish rarely, and coverage is denser in some places than in others — the record keeps its provenance so that skew stays visible instead of being smoothed away. Corrections append to the timeline rather than overwriting it, which is what makes an early reading safe to use.